Tidy data must be stored in data frames. We introduced the data frame in Section 2.4¹ and have been using the murders data frame throughout the book. In Section 4.7² we introduced the group_by function, which permits stratifying data before computing summary statistics. But where is the group information stored in the data frame?

murders %>% group_by(region)
#> # A tibble: 51 x 6
#> # Groups:   region [4]
#>   state      abb   region population total  rate
#>   <chr>      <chr> <fct>       <dbl> <dbl> <dbl>
#> 1 Alabama    AL    South     4779736   135  2.82
#> 2 Alaska     AK    West       710231    19  2.68
#> 3 Arizona    AZ    West      6392017   232  3.63
#> 4 Arkansas   AR    South     2915918    93  3.19
#> 5 California CA    West     37253956  1257  3.37
#> # … with 46 more rows

Notice that there are no columns with this information. But, if you look closely at the output above, you see the line A tibble followd by dimensions. We can learn the class of the returned object using:

murders %>% group_by(region) %>% class()
#> [1] "grouped_df" "tbl_df"     "tbl"        "data.frame"

The tbl, pronounced tibble, is a special kind of data frame. The functions group_by and summarize always return this type of data frame. The group_by function returns a special kind of tbl, the grouped_df. We will say more about these later. For consistency, the dplyr manipulation verbs (select, filter, mutate, and arrange) preserve the class of the input: if they receive a regular data frame they return a regular data frame, while if they receive a tibble they return a tibble. But tibbles are the preferred format in the tidyverse and as a result tidyverse functions that produce a data frame from scratch return a tibble. For example, in Chapter 5³ we will see that tidyverse functions used to import data create tibbles.

Tibbles are very similar to data frames. In fact, you can think of them as a modern version of data frames. Nonetheless there are three important differences which we describe next.

Tibbles display better

The print method for tibbles is more readable than that of a data frame. To see this, compare the outputs of typing murders and the output of murders if we convert it to a tibble. We can do this using as_tibble(murders). If using RStudio, output for a tibble adjusts to your window size. To see this, change the width of your R console and notice how more/less columns are shown.

Subsets of tibbles are tibbles

If you subset the columns of a data frame, you may get back an object that is not a data frame, such as a vector or scalar. For example:

class(murders[,4])
#> [1] "numeric"

is not a data frame. With tibbles this does not happen:

class(as_tibble(murders)[,4])
#> [1] "tbl_df"     "tbl"        "data.frame"

This is useful in the tidyverse since functions require data frames as input.

With tibbles, if you want to access the vector that defines a column, and not get back a data frame, you need to use the accessor $:

class(as_tibble(murders)$population)
#> [1] "numeric"

A related feature is that tibbles will give you a warning if you try to access a column that does not exist. If we accidentally write Population instead of population this:

murders$Population
#> NULL

returns a NULL with no warning, which can make it harder to debug. In contrast, if we try this with a tibble we get an informative warning:

as_tibble(murders)$Population
#> Warning: Unknown or uninitialised column: `Population`.
#> NULL

Tibbles can have complex entries

While data frame columns need to be vectors of numbers, strings, or logical values, tibbles can have more complex objects, such as lists or functions. Also, we can create tibbles with functions:

tibble(id = c(1, 2, 3), func = c(mean, median, sd))
#> # A tibble: 3 x 2
#>      id func  
#>   <dbl> <list>
#> 1     1 <fn>  
#> 2     2 <fn>  
#> 3     3 <fn>

Tibbles can be grouped

The function group_by returns a special kind of tibble: a grouped tibble. This class stores information that lets you know which rows are in which groups. The tidyverse functions, in particular the summarize function, are aware of the group information.

Create a tibble using tibble instead of data.frame

It is sometimes useful for us to create our own data frames. To create a data frame in the tibble format, you can do this by using the tibble function.

grades <- tibble(names = c("John", "Juan", "Jean", "Yao"), 
                     exam_1 = c(95, 80, 90, 85), 
                     exam_2 = c(90, 85, 85, 90))

Note that base R (without packages loaded) has a function with a very similar name, data.frame, that can be used to create a regular data frame rather than a tibble. One other important difference is that by default data.frame coerces characters into factors without providing a warning or message:

grades <- data.frame(names = c("John", "Juan", "Jean", "Yao"), 
                     exam_1 = c(95, 80, 90, 85), 
                     exam_2 = c(90, 85, 85, 90))
class(grades$names)
#> [1] "factor"

To avoid this, we use the rather cumbersome argument stringsAsFactors:

grades <- data.frame(names = c("John", "Juan", "Jean", "Yao"), 
                     exam_1 = c(95, 80, 90, 85), 
                     exam_2 = c(90, 85, 85, 90),
                     stringsAsFactors = FALSE)
class(grades$names)
#> [1] "character"

To convert a regular data frame to a tibble, you can use the as_tibble function.

as_tibble(grades) %>% class()
#> [1] "tbl_df"     "tbl"        "data.frame"